jtbd-analyze

To extract Jobs-To-Be-Done from technical documentation, you can parse markdown content using a defined methodology to identify and structure user motivations into actionable job statements, outputting the results as structured JSONL and CSV files.

Analyzing large product manuals for user stories is best handled via chunked subagent processing, which scales the extraction of job statements and outcomes efficiently without overwhelming the processing pipeline.

Yes, you can configure user research analysis for a specific domain by applying research configuration overlays, which tailor the Jobs-To-Be-Done extraction to your domain-specific requirements.

The JTBD extraction outputs structured JSONL and CSV files, providing structured content strategy analysis data that can be easily integrated into downstream product management workflows.

Yes, you need markdown files as the input source to identify user goals and motivations, because the parsing methodology specifically reads markdown content to extract structured job statements.

Automated job statement extraction transforms user goals buried in text into structured, actionable insights, enabling product management to align feature prioritization and content restructuring directly with user needs.